{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Your first neural network\n",
    "\n",
    "In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code, but left the implementation of the neural network up to you (for the most part). After you've submitted this project, feel free to explore the data and the model more.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load and prepare the data\n",
    "\n",
    "A critical step in working with neural networks is preparing the data correctly. Variables on different scales make it difficult for the network to efficiently learn the correct weights. Below, we've written the code to load and prepare the data. You'll learn more about this soon!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_path = 'Bike-Sharing-Dataset/hour.csv'\n",
    "\n",
    "rides = pd.read_csv(data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  hr  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1   0        0        6           0   \n",
       "1        2  2011-01-01       1   0     1   1        0        6           0   \n",
       "2        3  2011-01-01       1   0     1   2        0        6           0   \n",
       "3        4  2011-01-01       1   0     1   3        0        6           0   \n",
       "4        5  2011-01-01       1   0     1   4        0        6           0   \n",
       "\n",
       "   weathersit  temp   atemp   hum  windspeed  casual  registered  cnt  \n",
       "0           1  0.24  0.2879  0.81        0.0       3          13   16  \n",
       "1           1  0.22  0.2727  0.80        0.0       8          32   40  \n",
       "2           1  0.22  0.2727  0.80        0.0       5          27   32  \n",
       "3           1  0.24  0.2879  0.75        0.0       3          10   13  \n",
       "4           1  0.24  0.2879  0.75        0.0       0           1    1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rides.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking out the data\n",
    "\n",
    "This dataset has the number of riders for each hour of each day from January 1 2011 to December 31 2012. The number of riders is split between casual and registered, summed up in the `cnt` column. You can see the first few rows of the data above.\n",
    "\n",
    "Below is a plot showing the number of bike riders over the first 10 days or so in the data set. (Some days don't have exactly 24 entries in the data set, so it's not exactly 10 days.) You can see the hourly rentals here. This data is pretty complicated! The weekends have lower over all ridership and there are spikes when people are biking to and from work during the week. Looking at the data above, we also have information about temperature, humidity, and windspeed, all of these likely affecting the number of riders. You'll be trying to capture all this with your model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x216177d9b00>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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znnQ08RbFhQo+FXxCiB3bCvQspBh0RQghQbBbdPJJ0fn6XadGt+9evARfs55dRAW/z0FX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IBSn8HaWr+QMbbb6j8Amz28DldMcAq+twKfFsqiFVrRyFTw62PRqZ1ZTBV86r/eurRLaEFQiXZu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\ndm5i60+ePo1b7rwPT9d4rZRMvmgMfErEI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/nu3u/efPfc55xjJeIC38zMzMysRFzgm5mZ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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x216177d9898>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 263,
       "width": 380
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rides[:24*10].plot(x='dteday', y='cnt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dummy variables\n",
    "Here we have some categorical variables like season, weather, month. To include these in our model, we'll need to make binary dummy variables. This is simple to do with Pandas thanks to `get_dummies()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>yr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>temp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "      <th>season_1</th>\n",
       "      <th>season_2</th>\n",
       "      <th>...</th>\n",
       "      <th>hr_21</th>\n",
       "      <th>hr_22</th>\n",
       "      <th>hr_23</th>\n",
       "      <th>weekday_0</th>\n",
       "      <th>weekday_1</th>\n",
       "      <th>weekday_2</th>\n",
       "      <th>weekday_3</th>\n",
       "      <th>weekday_4</th>\n",
       "      <th>weekday_5</th>\n",
       "      <th>weekday_6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 59 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   yr  holiday  temp   hum  windspeed  casual  registered  cnt  season_1  \\\n",
       "0   0        0  0.24  0.81        0.0       3          13   16         1   \n",
       "1   0        0  0.22  0.80        0.0       8          32   40         1   \n",
       "2   0        0  0.22  0.80        0.0       5          27   32         1   \n",
       "3   0        0  0.24  0.75        0.0       3          10   13         1   \n",
       "4   0        0  0.24  0.75        0.0       0           1    1         1   \n",
       "\n",
       "   season_2    ...      hr_21  hr_22  hr_23  weekday_0  weekday_1  weekday_2  \\\n",
       "0         0    ...          0      0      0          0          0          0   \n",
       "1         0    ...          0      0      0          0          0          0   \n",
       "2         0    ...          0      0      0          0          0          0   \n",
       "3         0    ...          0      0      0          0          0          0   \n",
       "4         0    ...          0      0      0          0          0          0   \n",
       "\n",
       "   weekday_3  weekday_4  weekday_5  weekday_6  \n",
       "0          0          0          0          1  \n",
       "1          0          0          0          1  \n",
       "2          0          0          0          1  \n",
       "3          0          0          0          1  \n",
       "4          0          0          0          1  \n",
       "\n",
       "[5 rows x 59 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy_fields = ['season', 'weathersit', 'mnth', 'hr', 'weekday']\n",
    "for each in dummy_fields:\n",
    "    dummies = pd.get_dummies(rides[each], prefix=each, drop_first=False)\n",
    "    rides = pd.concat([rides, dummies], axis=1)\n",
    "\n",
    "fields_to_drop = ['instant', 'dteday', 'season', 'weathersit', \n",
    "                  'weekday', 'atemp', 'mnth', 'workingday', 'hr']\n",
    "data = rides.drop(fields_to_drop, axis=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scaling target variables\n",
    "To make training the network easier, we'll standardize each of the continuous variables. That is, we'll shift and scale the variables such that they have zero mean and a standard deviation of 1.\n",
    "\n",
    "The scaling factors are saved so we can go backwards when we use the network for predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "quant_features = ['casual', 'registered', 'cnt', 'temp', 'hum', 'windspeed']\n",
    "# Store scalings in a dictionary so we can convert back later\n",
    "scaled_features = {}\n",
    "for each in quant_features:\n",
    "    mean, std = data[each].mean(), data[each].std()\n",
    "    scaled_features[each] = [mean, std]\n",
    "    data.loc[:, each] = (data[each] - mean)/std"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting the data into training, testing, and validation sets\n",
    "\n",
    "We'll save the data for the last approximately 21 days to use as a test set after we've trained the network. We'll use this set to make predictions and compare them with the actual number of riders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Save data for approximately the last 21 days \n",
    "test_data = data[-21*24:]\n",
    "\n",
    "# Now remove the test data from the data set \n",
    "data = data[:-21*24]\n",
    "\n",
    "# Separate the data into features and targets\n",
    "target_fields = ['cnt', 'casual', 'registered']\n",
    "features, targets = data.drop(target_fields, axis=1), data[target_fields]\n",
    "test_features, test_targets = test_data.drop(target_fields, axis=1), test_data[target_fields]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll split the data into two sets, one for training and one for validating as the network is being trained. Since this is time series data, we'll train on historical data, then try to predict on future data (the validation set)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Hold out the last 60 days or so of the remaining data as a validation set\n",
    "train_features, train_targets = features[:-60*24], targets[:-60*24]\n",
    "val_features, val_targets = features[-60*24:], targets[-60*24:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time to build the network\n",
    "\n",
    "Below you'll build your network. We've built out the structure and the backwards pass. You'll implement the forward pass through the network. You'll also set the hyperparameters: the learning rate, the number of hidden units, and the number of training passes.\n",
    "\n",
    "<img src=\"assets/neural_network.png\" width=300px>\n",
    "\n",
    "The network has two layers, a hidden layer and an output layer. The hidden layer will use the sigmoid function for activations. The output layer has only one node and is used for the regression, the output of the node is the same as the input of the node. That is, the activation function is $f(x)=x$. A function that takes the input signal and generates an output signal, but takes into account the threshold, is called an activation function. We work through each layer of our network calculating the outputs for each neuron. All of the outputs from one layer become inputs to the neurons on the next layer. This process is called *forward propagation*.\n",
    "\n",
    "We use the weights to propagate signals forward from the input to the output layers in a neural network. We use the weights to also propagate error backwards from the output back into the network to update our weights. This is called *backpropagation*.\n",
    "\n",
    "> **Hint:** You'll need the derivative of the output activation function ($f(x) = x$) for the backpropagation implementation. If you aren't familiar with calculus, this function is equivalent to the equation $y = x$. What is the slope of that equation? That is the derivative of $f(x)$.\n",
    "\n",
    "Below, you have these tasks:\n",
    "1. Implement the sigmoid function to use as the activation function. Set `self.activation_function` in `__init__` to your sigmoid function.\n",
    "2. Implement the forward pass in the `train` method.\n",
    "3. Implement the backpropagation algorithm in the `train` method, including calculating the output error.\n",
    "4. Implement the forward pass in the `run` method.\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class NeuralNetwork(object):\n",
    "    def __init__(self, input_nodes, hidden_nodes, output_nodes, learning_rate):\n",
    "        # Set number of nodes in input, hidden and output layers.\n",
    "        self.input_nodes = input_nodes\n",
    "        self.hidden_nodes = hidden_nodes\n",
    "        self.output_nodes = output_nodes\n",
    "\n",
    "        # Initialize weights\n",
    "        self.weights_input_to_hidden = np.random.normal(0.0, self.input_nodes**-0.5, \n",
    "                                       (self.input_nodes, self.hidden_nodes))\n",
    "\n",
    "        self.weights_hidden_to_output = np.random.normal(0.0, self.hidden_nodes**-0.5, \n",
    "                                       (self.hidden_nodes, self.output_nodes))\n",
    "        self.lr = learning_rate\n",
    "        \n",
    "        #### TODO: Set self.activation_function to your implemented sigmoid function ####\n",
    "        #\n",
    "        # Note: in Python, you can define a function with a lambda expression,\n",
    "        # as shown below.\n",
    "        self.activation_function = lambda x : 1/(1 + np.exp(-x))  # Replace 0 with your sigmoid calculation.\n",
    "        \n",
    "        ### If the lambda code above is not something you're familiar with,\n",
    "        # You can uncomment out the following three lines and put your \n",
    "        # implementation there instead.\n",
    "        #\n",
    "        #def sigmoid(x):\n",
    "        #    return 0  # Replace 0 with your sigmoid calculation here\n",
    "        #self.activation_function = sigmoid\n",
    "                    \n",
    "    \n",
    "    def train(self, features, targets):\n",
    "        ''' Train the network on batch of features and targets. \n",
    "        \n",
    "            Arguments\n",
    "            ---------\n",
    "            \n",
    "            features: 2D array, each row is one data record, each column is a feature\n",
    "            targets: 1D array of target values\n",
    "        \n",
    "        '''\n",
    "        n_records = features.shape[0]\n",
    "        delta_weights_i_h = np.zeros(self.weights_input_to_hidden.shape)\n",
    "        delta_weights_h_o = np.zeros(self.weights_hidden_to_output.shape)\n",
    "        for X, y in zip(features, targets):\n",
    "            #### Implement the forward pass here ####\n",
    "            ### Forward pass ###\n",
    "            # TODO: Hidden layer - Replace these values with your calculations.\n",
    "            hidden_inputs = np.dot(X, self.weights_input_to_hidden) # signals into hidden layer\n",
    "            hidden_outputs = self.activation_function(hidden_inputs) # signals from hidden layer\n",
    "\n",
    "            # TODO: Output layer - Replace these values with your calculations.\n",
    "            final_inputs = np.dot(hidden_outputs, self.weights_hidden_to_output) # signals into final output layer\n",
    "            final_outputs = final_inputs # signals from final output layer\n",
    "            \n",
    "            #### Implement the backward pass here ####\n",
    "            ### Backward pass ###\n",
    "\n",
    "            # TODO: Output error - Replace this value with your calculations.\n",
    "            error = y - final_outputs # Output layer error is the difference between desired target and actual output.\n",
    "            \n",
    "            # TODO: Calculate the hidden layer's contribution to the error\n",
    "            hidden_error = np.dot(self.weights_hidden_to_output, error)\n",
    "            # TODO: Backpropagated error terms - Replace these values with your calculations.\n",
    "            output_error_term = error\n",
    "            hidden_error_term = hidden_error*hidden_outputs*(1 - hidden_outputs)\n",
    "\n",
    "            # Weight step (input to hidden)\n",
    "            delta_weights_i_h += hidden_error_term * X[:,None]\n",
    "            # Weight step (hidden to output)\n",
    "            delta_weights_h_o += output_error_term * hidden_outputs[:,None]\n",
    "\n",
    "        # TODO: Update the weights - Replace these values with your calculations.\n",
    "        self.weights_hidden_to_output += self.lr * delta_weights_h_o / n_records # update hidden-to-output weights with gradient descent step\n",
    "        self.weights_input_to_hidden += self.lr * delta_weights_i_h / n_records # update input-to-hidden weights with gradient descent step\n",
    " \n",
    "    def run(self, features):\n",
    "        ''' Run a forward pass through the network with input features \n",
    "        \n",
    "            Arguments\n",
    "            ---------\n",
    "            features: 1D array of feature values\n",
    "        '''\n",
    "        \n",
    "        #### Implement the forward pass here ####\n",
    "        # TODO: Hidden layer - replace these values with the appropriate calculations.\n",
    "        hidden_inputs = np.dot(features, self.weights_input_to_hidden) # signals into hidden layer\n",
    "        hidden_outputs = self.activation_function(hidden_inputs) # signals from hidden layer\n",
    "        \n",
    "        # TODO: Output layer - Replace these values with the appropriate calculations.\n",
    "        final_inputs = np.dot(hidden_outputs, self.weights_hidden_to_output) # signals into final output layer\n",
    "        final_outputs = final_inputs # signals from final output layer \n",
    "        \n",
    "        return final_outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def MSE(y, Y):\n",
    "    return np.mean((y-Y)**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Unit tests\n",
    "\n",
    "Run these unit tests to check the correctness of your network implementation. This will help you be sure your network was implemented correctly befor you starting trying to train it. These tests must all be successful to pass the project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      ".....\n",
      "----------------------------------------------------------------------\n",
      "Ran 5 tests in 0.025s\n",
      "\n",
      "OK\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<unittest.runner.TextTestResult run=5 errors=0 failures=0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import unittest\n",
    "\n",
    "inputs = np.array([[0.5, -0.2, 0.1]])\n",
    "targets = np.array([[0.4]])\n",
    "test_w_i_h = np.array([[0.1, -0.2],\n",
    "                       [0.4, 0.5],\n",
    "                       [-0.3, 0.2]])\n",
    "test_w_h_o = np.array([[0.3],\n",
    "                       [-0.1]])\n",
    "\n",
    "class TestMethods(unittest.TestCase):\n",
    "    \n",
    "    ##########\n",
    "    # Unit tests for data loading\n",
    "    ##########\n",
    "    \n",
    "    def test_data_path(self):\n",
    "        # Test that file path to dataset has been unaltered\n",
    "        self.assertTrue(data_path.lower() == 'bike-sharing-dataset/hour.csv')\n",
    "        \n",
    "    def test_data_loaded(self):\n",
    "        # Test that data frame loaded\n",
    "        self.assertTrue(isinstance(rides, pd.DataFrame))\n",
    "    \n",
    "    ##########\n",
    "    # Unit tests for network functionality\n",
    "    ##########\n",
    "\n",
    "    def test_activation(self):\n",
    "        network = NeuralNetwork(3, 2, 1, 0.5)\n",
    "        # Test that the activation function is a sigmoid\n",
    "        self.assertTrue(np.all(network.activation_function(0.5) == 1/(1+np.exp(-0.5))))\n",
    "\n",
    "    def test_train(self):\n",
    "        # Test that weights are updated correctly on training\n",
    "        network = NeuralNetwork(3, 2, 1, 0.5)\n",
    "        network.weights_input_to_hidden = test_w_i_h.copy()\n",
    "        network.weights_hidden_to_output = test_w_h_o.copy()\n",
    "        \n",
    "        network.train(inputs, targets)\n",
    "        self.assertTrue(np.allclose(network.weights_hidden_to_output, \n",
    "                                    np.array([[ 0.37275328], \n",
    "                                              [-0.03172939]])))\n",
    "        self.assertTrue(np.allclose(network.weights_input_to_hidden,\n",
    "                                    np.array([[ 0.10562014, -0.20185996], \n",
    "                                              [0.39775194, 0.50074398], \n",
    "                                              [-0.29887597, 0.19962801]])))\n",
    "\n",
    "    def test_run(self):\n",
    "        # Test correctness of run method\n",
    "        network = NeuralNetwork(3, 2, 1, 0.5)\n",
    "        network.weights_input_to_hidden = test_w_i_h.copy()\n",
    "        network.weights_hidden_to_output = test_w_h_o.copy()\n",
    "\n",
    "        self.assertTrue(np.allclose(network.run(inputs), 0.09998924))\n",
    "\n",
    "suite = unittest.TestLoader().loadTestsFromModule(TestMethods())\n",
    "unittest.TextTestRunner().run(suite)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the network\n",
    "\n",
    "Here you'll set the hyperparameters for the network. The strategy here is to find hyperparameters such that the error on the training set is low, but you're not overfitting to the data. If you train the network too long or have too many hidden nodes, it can become overly specific to the training set and will fail to generalize to the validation set. That is, the loss on the validation set will start increasing as the training set loss drops.\n",
    "\n",
    "You'll also be using a method know as Stochastic Gradient Descent (SGD) to train the network. The idea is that for each training pass, you grab a random sample of the data instead of using the whole data set. You use many more training passes than with normal gradient descent, but each pass is much faster. This ends up training the network more efficiently. You'll learn more about SGD later.\n",
    "\n",
    "### Choose the number of iterations\n",
    "This is the number of batches of samples from the training data we'll use to train the network. The more iterations you use, the better the model will fit the data. However, if you use too many iterations, then the model with not generalize well to other data, this is called overfitting. You want to find a number here where the network has a low training loss, and the validation loss is at a minimum. As you start overfitting, you'll see the training loss continue to decrease while the validation loss starts to increase.\n",
    "\n",
    "### Choose the learning rate\n",
    "This scales the size of weight updates. If this is too big, the weights tend to explode and the network fails to fit the data. A good choice to start at is 0.1. If the network has problems fitting the data, try reducing the learning rate. Note that the lower the learning rate, the smaller the steps are in the weight updates and the longer it takes for the neural network to converge.\n",
    "\n",
    "### Choose the number of hidden nodes\n",
    "The more hidden nodes you have, the more accurate predictions the model will make. Try a few different numbers and see how it affects the performance. You can look at the losses dictionary for a metric of the network performance. If the number of hidden units is too low, then the model won't have enough space to learn and if it is too high there are too many options for the direction that the learning can take. The trick here is to find the right balance in number of hidden units you choose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Progress: 100.0% ... Training loss: 0.266 ... Validation loss: 0.430"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "\n",
    "### Set the hyperparameters here ###\n",
    "iterations = 3000\n",
    "learning_rate = 0.1\n",
    "hidden_nodes = 17\n",
    "output_nodes = 1\n",
    "\n",
    "N_i = train_features.shape[1]\n",
    "network = NeuralNetwork(N_i, hidden_nodes, output_nodes, learning_rate)\n",
    "\n",
    "losses = {'train':[], 'validation':[]}\n",
    "for ii in range(iterations):\n",
    "    # Go through a random batch of 128 records from the training data set\n",
    "    batch = np.random.choice(train_features.index, size=128)\n",
    "    X, y = train_features.ix[batch].values, train_targets.ix[batch]['cnt']\n",
    "                             \n",
    "    network.train(X, y)\n",
    "    \n",
    "    # Printing out the training progress\n",
    "    train_loss = MSE(network.run(train_features).T, train_targets['cnt'].values)\n",
    "    val_loss = MSE(network.run(val_features).T, val_targets['cnt'].values)\n",
    "    sys.stdout.write(\"\\rProgress: {:2.1f}\".format(100 * ii/float(iterations)) \\\n",
    "                     + \"% ... Training loss: \" + str(train_loss)[:5] \\\n",
    "                     + \" ... Validation loss: \" + str(val_loss)[:5])\n",
    "    sys.stdout.flush()\n",
    "    \n",
    "    losses['train'].append(train_loss)\n",
    "    losses['validation'].append(val_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4vXr1YuLEiVx11VU8/fTTjB07lvbt23PIIYdw8cUXV7lbTkPr378/b731Fldd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\n/wc4C/T3VNfiHwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21618196b00>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 380
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(losses['train'], label='Training loss')\n",
    "plt.plot(losses['validation'], label='Validation loss')\n",
    "plt.legend()\n",
    "_ = plt.ylim()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check out your predictions\n",
    "\n",
    "Here, use the test data to view how well your network is modeling the data. If something is completely wrong here, make sure each step in your network is implemented correctly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mfdfGx/8L3Au8FrgT2Bs4D3gj8H1aw+h2anx8LuHxmrfv3OPx07LWHhJ3eyMD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or3JOV4zodkyWY9qdccnjHZczxpsnrRemuUZ0jGkfAdoL1LXTMZqCdnqdtVRgTIFSQ6lpV6NO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\nidu9M57m+69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62G8+t88Mgvy3GzeIH1T1sxtgDFh8Dn++/yHhx8LYN4/2eGFOe7162gEp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45WPC04WvGU40pMUoAx+trKN8rnblyDn7sxH9gq/p7yRA2/AmYAW1R+16yOVjzl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\nQgghhBBCCFFQ/j8CcqTYhZwhFAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21617a862e8>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 272,
       "width": 502
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8,4))\n",
    "\n",
    "mean, std = scaled_features['cnt']\n",
    "predictions = network.run(test_features).T*std + mean\n",
    "ax.plot(predictions[0], label='Prediction')\n",
    "ax.plot((test_targets['cnt']*std + mean).values, label='Data')\n",
    "ax.set_xlim(right=len(predictions))\n",
    "ax.legend()\n",
    "\n",
    "dates = pd.to_datetime(rides.ix[test_data.index]['dteday'])\n",
    "dates = dates.apply(lambda d: d.strftime('%b %d'))\n",
    "ax.set_xticks(np.arange(len(dates))[12::24])\n",
    "_ = ax.set_xticklabels(dates[12::24], rotation=45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## OPTIONAL: Thinking about your results(this question will not be evaluated in the rubric).\n",
    " \n",
    "Answer these questions about your results. How well does the model predict the data? Where does it fail? Why does it fail where it does?\n",
    "\n",
    "> **Note:** You can edit the text in this cell by double clicking on it. When you want to render the text, press control + enter\n",
    "\n",
    "#### Your answer below"
   ]
  }
 ],
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